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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Computer Science and Informatics Engineering (J-Cosine) Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi
Ario Yudo Husodo, Ario Yudo
Departmet of Informatics Engineering, Engineering Faculty, Mataram University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Materials Science & Nanotechnology Physics

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
PERFORMANCE COMPARISON OF NAIVE BAYES AND BIDIRECTIONAL LSTM ALGORITHMS IN BSI MOBILE REVIEW SENTIMENT ANALYSIS Ma'we, Hannatul; Husodo, Ario Yudo; Irmawati, Budi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4178

Abstract

Currently, almost all banks have used mobile banking in conducting banking transactions, one of which is Bank Syariah Indonesia (BSI). BSI mobile is still classified as a new mobile banking application compared to other mobile banking, this certainly still has a low rating and really needs feedback from users which can be seen through reviews on the Google Play Store application. Input in the form of criticism and suggestions from BSI mobile users can be used by BSI mobile as a suggestion for careful supervision and evaluation material in improving its services. This study aims to find the best algorithm to analyze review sentiment on the Google Play Store for the BSI mobile application and provide an overview of the response of application users to application developers based on the results of review data processing. The data mining methodology used in this study is CRISP-DM, using a dataset collected for 6 years (2018-2023) which is annotated into positive and negative labels manually, then modeled using 2 algorithms, namely Naïve Bayes (NB) and Bidirectional LSTM (BiLSTM). The contribution of this study is to test, evaluate and compare the two algorithms (NB and BiLSTM) using the K-Fold Cross Validation (NB) testing model and over-sampling techniques to the minority class (negative) then provide recommendations for the best algorithm. The conclusion of the study is that the BiLSTM algorithm is superior to NB with an accuracy of 94.90 % while the NB algorithm is 94%. In addition, the over-sampling technique is more optimal in increasing the accuracy of the algorithm's performance compared to without over-sampling.
IMPROVING SHOPPING EXPERIENCES AT NTB MALL THROUGH PERSONALIZED PRODUCT RECOMMENDATIONS USING CONTENT-BASED FILTERING Husodo, Ario Yudo; Bimantoro, Fitri; Agitha, Nadiyasari; Grendis, Nuraqilla Waidha Bintang
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4194

Abstract

NTB MALL, an e-commerce platform specializing in unique products from micro, small, and medium enterprises (MSMEs) in West Nusa Tenggara, faces challenges in providing personalized product recommendations due to the diversity of its product categories and consumer preferences. To address this, this study implements a content-based filtering (CBF) approach utilizing Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity to enhance recommendation accuracy. The system analyzes product attributes and user interaction history to generate tailored suggestions. Experimental results indicate that cosine similarity outperforms Euclidean distance in recommendation precision, achieving an accuracy of 89% and a Mean Reciprocal Rank (MRR) of 95%. Furthermore, user feedback reveals that 93% of users found the recommendations highly relevant, 89% reported increased engagement, and 96% expressed satisfaction with the personalized shopping experience. This research provides a novel application of AI-driven recommendation systems in regional e-commerce marketplaces, demonstrating their potential to improve user experience and foster stronger connections between consumers and local producers.
Development of a Convolutional Neural Network Method for Classifying Ripeness Levels of Servo Variety Tomatoes Rosalina, Rosalina; Husodo, Ario Yudo; Wijaya, I Gede Pasek Suta
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4168

Abstract

The distribution of tomatoes in Indonesia is huge, making it an important commodity in the agricultural sector. However, manual classification of tomato ripeness can lead to human error and decrease supply chain efficiency. Therefore, an automated system capable of classifying tomatoes quickly and accurately is needed, in order to reduce the potential for human error and improve supply chain efficiency. This research aims to develop the Convolutional Neural Network (CNN) method to improve the accuracy of tomato ripeness detection through modifications to the architecture, such as reducing several layers, adding batch normalization, and adding dropouts. The dataset used in this study consists of 500 images taken by the researcher himself which are divided into 5 classes, namely unriped, half-riped, riped, half-rotten, and rotten, with each class containing 100 images. There are 3 proposed CNN models, namely the standard model, as well as the addition of batch normalization and dropout in the architecture. The results showed that the proposed model 3 with the addition of dropout on several layers of its architecture is the optimal model with a parameter of 2.4 million and using a batch size of 16 resulting in an accuracy of 98%, as well as precision, recall, and F1-score values of 98%. With these results, the proposed CNN model is effective in identifying the ripeness level of tomato fruit. This research is expected to be applied in the agricultural industry to improve the efficiency of sorting and distributing tomato fruits according to the desired quality standards.
Enhanced Identity Recognition Through the Development of a Convolutional Neural Network Using Indonesian Palmprints Aprilla, Diah Mitha; Husodo, Ario Yudo; Wijaya, I Gede Pasek Suta
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4169

Abstract

The use of palmprint as an identification system has gained significant attention due to its potential in biometric authentication. However, existing models often face challenges related to computational complexity and the ability to scale with larger datasets. This research aims to develop an efficient Convolutional Neural Network (CNN) model for palmprint identity recognition, specifically tailored to address these challenges. A novel contribution of this study is the creation of an original palmprint dataset consisting of 700 images from 50 Indonesian college students, which serves as a foundation for future research in Southeast Asia. The dataset includes different scenarios with varying input sizes (32x32, 64x64, 96x96 pixels) and the number of classes (30, 40, 50) to assess the model's scalability and performance. Three CNN architectures were designed with varying layers, activation functions, and dropout strategies to capture the unique features of palmprints and improve model generalization. The results show that the best-performing model, Model 3, which incorporates dropout layers, achieved 95% accuracy, 96% precision, 95% recall, and 95% F1-score on 50 classes with 1.2 million parameters. Model 1 achieved 98% accuracy, 99% precision, 98% recall, and 98% F1-score on 40 classes with 1.7 million parameters. These findings demonstrate that the proposed CNN models not only achieve high accuracy but also maintain computational efficiency, offering promising solutions for real-time palmprint authentication systems. This research contributes to the advancement of biometric authentication systems, with significant implications for real- world applications in Southeast Asia.
Modification Of Yolov11 Nano And Small Architecture For Improved Accuracy In Motorcycle Riders Face Recognition Based On Eye Ardiansyah, Randy; Wirarama WW, I Gde Putu; Husodo, Ario Yudo
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4535

Abstract

Face recognition still faces challenges in identifying faces covered by masks and helmets with open visors, such as those commonly used by motorcyclists, especially when entering parking areas. To improve the accuracy of face recognition in these conditions, this study proposes nano and small versions of the YOLOv11 modification, which is an internal version. Modifications are made to the neck section and the DySample module is added in place of the UpSample module to improve the model's capabilities. Experiments were conducted using a self-generated dataset consisting of 50 classes. The results show that the modified nano version achieves 99.3% accuracy at the same mAP50 as YOLOv11n and YOLOv12n. At mAP50-95, it shows a 1.6% accuracy improvement compared to YOLOv11n and YOLOv12n with 75% accuracy. Meanwhile, the modified small version achieved an accuracy improvement of 1.3% and 1.2% compared to YOLOv11n and YOLOv12n, respectively, reaching 76.1% on mAP50-95, although the accuracy on mAP50 remained the same as YOLOv11n and 0.1% superior to YOLOv12n. However, recall and precision did not show significant improvement in both as well as the increase in model parameters. However, the model is still in the nano and small versions. Therefore, the model can be implemented on edge devices. This research is important for the field of computer vision, especially in the context of face recognition. The contribution of this research is the improvement of the accuracy of the mAP50-95 metric in eye-based face recognition, which is relevant for intelligent security systems with limited resources.
Incremental CNN-k-NN Hybrid Facial Recognition for Helmeted Facial Recognition in IoT-Enabled Smart Parking: A Case Study at Universitas Mataram Widiartha, Ida Bagus Ketut; Husodo, Ario Yudo; Thuy, Tran Thi Thanh; Murpratiwi, Santi Ika
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5447

Abstract

Helmeted rider identification challenges traditional facial recognition, especially in Indonesian campuses like UNRAM, where motorbike use is prevalent and theft risks are high. This study develops a hybrid CNN-k-NN system for secure parking access. The dataset contains 2,800 augmented images (Haar Cascade crop, 224x224 grayscale), with features extracted via VGG16/ResNet and classified using k-NN (k=1, Euclidean/Cosine). The system achieves 95.62% accuracy, with precision, recall, and F1 scores of 0.96. Incremental retraining reduces processing time to under 1 second, compared to 30 minutes for full retraining. The use of cosine similarity improves accuracy slightly over Euclidean distance. This solution enhances IoT-based smart campuses by enabling efficient, real-time identification and reducing theft by improving access control. It is adaptable to low-resource environments, supporting scalable deployments in smart parking and campus security systems.
Co-Authors Afwani, Royana Agitha, Nadiyasari Aprilla, Diah Mitha Ardiansyah, Randy Budi Irmawati Darmawan, Riski Dwiyansaputra, Ramaditia Fitri Bimantoro Grendis, Nuraqilla Waidha Bintang Halil Akhyar Hamidi, Mohammad Zaenuddin Hanan, Dimas Afryzal Heri Wijayanto I B K Widiartha I Gede Pasek Suta Wijaya I Wayan Agus Arimbawa, I Wayan Agus Ma'we, Hannatul Muhammad, David Arizaldi Muntari Muntari Murpratiwi, Santi Ika Nugraha, Gibran Satya Rabbani, Budiman Rahayu, Irma Putri Rahayu, Sefani Cahyo Auliya Raihan, Muhammad Dzulhi Rassy, Regania Pasca Rosalina Rosalina Sugianti, Novalia Dwirohmatun Thuy, Tran Thi Thanh Wirarama WW, I Gde Putu